We have identified a wide range of possible applications of causality in medical imaging. However, we have not yet observed an enthusiastic uptake of causal reasoning and causal considerations from the community. We believe that introducing causal reasoning in medical imaging applications would benefit both the performance and robustness of the algorithms but most of all would provide the required scrutiny and security that doctors demand from their tools. A model that is able to be probed causally and does not operate as a black box can be trusted more easily by healthcare professionals since they will be able to understand its inner workings; enabling safe human-machine decision making [1]}. Moreover it would ameliorate the legal hurdle of accountability that developers of medical ML applications face, as we would be able to explain the processes and logic of the models.
